In this paper, a high-precision image authentication scheme for absolute moment block truncation coding (AMBTC)-compressed images is presented. For each block, two sub-bitmaps are conducted using the symmetrical separation, and the six-bit authentication code is symmetrically assigned to two sub-codes, which is virtually embedded into sub-bitmaps using the matrix encoding later. To overcome distortion caused by modifications to the bitmap, the corresponding to-be-flipped bit-location information is recorded instead of flipping these bits of the bitmap directly. Then, the bit-location information is inserted into quantization levels based on adjusted quantization level matching. In contrast to previous studies, the proposed scheme offers a significantly improved tampering detection ability, especially in the first hierarchical tampering detection without remediation measures, with an average tampering detection rate of up to 98.55%. Experimental results show that our approach provides a more stable and reliable tampering detection performance and sustains an acceptable visual quality.

This paper proposes a novel authentication scheme for absolute moment block truncation coding (AMBTC) of a compressed image using turtle shell based data hiding. For simplicity, we call it turtle shell based image authentication method (TSIA in short). For each block, a 3-bit authentication code (AC) is generated by combining the bitmap with a pseudo-random sequence and is concealed into the corresponding quantization levels with the use of a reference matrix. In order to solve the problem of having a high quantization level lower or equal to a low quantization level caused after the hiding operation, an iterative embedding mechanism is employed in the proposed TSIA scheme. Experimental results demonstrate that the proposed TSIA scheme outperforms previous works in the watermarked image quality with an increased PSNR of 0.16 dB and achieves high tampering detecting accuracy.INDEX TERMS Image authentication, AMBTC, compressed code, turtle shell based data hiding. I. INTRODUCTION

Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients. Twenty-one post-stroke patients and twenty-one healthy volunteers participated in the walking trials. Three of the most representative gait data, i.e., marker trajectory (MT), ground reaction force (GRF), and electromyogram, were simultaneously acquired from these subjects during their walking. A multimodal fusion architecture is established by using these different modal data to qualitatively distinguish the hemiparetic gait from normal gait by different pattern recognition techniques and to quantitatively estimate the patient's lower limb motor function by a novel probability-based gait score. Seven decision fusion algorithms have been tested in this architecture, and extensive data analysis experiments have been conducted. The results indicate that the recognition performance and estimation performance of the system become better when more modal gait data are fused. For the recognition performance, the random forest classifier based on the GRF data achieves an accuracy of 92.26% outperformed other single-modal schemes. When combining two modal data, the accuracy can be enhanced to 95.83% by using the support vector machine (SVM) fusion algorithm to fuse the MT and GRF data. When integrating all the three modal data, the accuracy can be further improved to 98.21% by using the SVM fusion algorithm. For the estimation performance, the absolute values of the correlation coefficients between the estimation results of the above three schemes and the Wisconsin gait scale scores for the post-stroke patients are 0.63, 0.75, and 0.84, respectively, which means the clinical relevance becomes more obvious when using more modalities. These promising results demonstrate that the proposed method has considerable potential to promote the future design of automatic gait analysis systems for clinical practice.

Recently, reversible data hiding in encrypted compressed images (RDHECI) has attracted more attention due to privacy information protection concerns. Meanwhile, AMBTC as one technique of lossy image compression that has lower storage costs and the simplicity in computation and is extensively used in many applications. Hence, this paper proposes an RDHECI scheme based on AMBTC, named O-AMBTC, to address privacy concerns. First, the original image is scrambled in a block-wise manner to generate the scrambled image which then is compressed by an AMBTC compression technique. Subsequently, the derived AMBTC compression codes are encrypted by using the methods of value modulation and stream cipher while the correlations between two quantization levels of AMBTC compression codes are retained and exploited to vacate redundant room to embed secret messages. Data hiding is then performed with the use of PBTL labeling strategy. In addition, another modified AMBTC compression code based RDHECI scheme, called M-AMBTC, is suggested to increase the ability to carry secret messages. In our dual approach, both the AMBTC-compressed image and the secret messages can be correctly recovered. Experimental results show that two proposed schemes are able to achieve average embedding rates as large as 0.6 bpp and 0.8 bpp when the block size is set to 2 × 2, respectively.

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It is increasingly important to achieve security and anti-counterfeiting for medical images. The integrity and verifiability of medical images before they are reviewed for potential medical diagnoses is imperative. This paper presents an effective self-embedding fragile watermarking scheme for medical images. Both self-recovery information and authentication code for each block are generated in advance and then embedded into other blocks separately with the help of the turtle shell and embedding table. In the tampering detection and localization phase, a three-level detection and localization strategy is employed to achieve higher accuracy for tampering localization. In the self-recovery phase, a two-hierarchical restoration method is exploited to achieve higher image quality in the recovered images. Several malicious attacks were tested to demonstrate the performance of the proposed scheme in terms of tampering detection and image recovery. Simulation results confirmed that the tampering detection rate can be up to 99.83% and the average PSNR of a recovered image can be up to 42.11 dB, both of which are higher than those of the Shehab et al.'s method. INDEX TERMS Medical images, fragile watermarking, authentication, self-recovery, turtle shell-based data hiding (TSDH)

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In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness.